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Fix LISA init OOM caused by deepspeed #728

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Merged
research4pan merged 2 commits into from
Apr 1, 2024
Merged

Fix LISA init OOM caused by deepspeed #728

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942e5b8
Fix OOM problem during init
research4pan Apr 1, 2024
d588b5a
Add paged_adamw to avoid occasional OOM
research4pan Apr 1, 2024
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13 changes: 4 additions & 9 deletions scripts/run_finetune_with_lisa.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@ dataset_path=data/alpaca/train
output_dir=output_models/finetune_lisa
lisa_activated_layers=1
lisa_interval_steps=20
deepspeed_args="--master_port=11000"

# Other optional arguments that can improve memory saving
gradient_checkpointing=True
Expand Down Expand Up @@ -41,10 +40,6 @@ while [[ $# -ge 1 ]]; do
output_dir="$2"
shift
;;
--deepspeed_args)
deepspeed_args="$2"
shift
;;
--lisa_activated_layers)
lisa_activated_layers="$2"
shift
Expand Down Expand Up @@ -90,8 +85,7 @@ project_dir=$(cd "$(dirname $0)"/..; pwd)
log_dir=${project_dir}/log/${exp_id}
mkdir -p ${output_dir} ${log_dir}

deepspeed ${deepspeed_args} \
examples/finetune.py \
python examples/finetune.py \
--model_name_or_path ${model_name_or_path} \
--dataset_path ${dataset_path} \
--output_dir ${output_dir} --overwrite_output_dir \
Expand All @@ -100,9 +94,10 @@ deepspeed ${deepspeed_args} \
--disable_group_texts 1 \
--block_size ${block_size} \
--per_device_train_batch_size ${per_device_train_batch_size} \
--deepspeed ${ds_config_file} \
--fp16 \
--bf16 \
--torch_dtype bfloat16 \
--run_name finetune \
--optim paged_adamw_32bit \
--validation_split_percentage 0 \
--logging_steps 20 \
--do_train \
Expand Down
1 change: 0 additions & 1 deletion src/lmflow/models/hf_decoder_model.py
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Expand Up @@ -242,7 +242,6 @@ def __init__(
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
quantization_config=quant_config if model_args.use_qlora else None,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
Expand Down
3 changes: 1 addition & 2 deletions src/lmflow/pipeline/finetuner.py
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Expand Up @@ -317,7 +317,6 @@ def __init__(self, n_layers, interval_steps, model):
self.layers_attribute = 'model.transformer.h' # General access path
self.total_layers = len(eval('self.' + self.layers_attribute)) # Dynamically execute to get the number of layers

self.switch_active_layers()
self.active_layers_indices = []

def freeze_all_layers(self):
Expand All @@ -338,7 +337,7 @@ def switch_active_layers(self):
# Randomly select n_layers to activate
layers = eval('self.' + self.layers_attribute) # Re-fetch layer references
self.active_layers_indices = np.random.choice(range(self.total_layers), self.n_layers, replace=False)
print(f"Activating layers at indices: {self.active_layers_indices} for the next steps.")
print(f"Activating layers at indices: {self.active_layers_indices} for the next steps.", flush=True)

# Enable gradients only for the selected layers
for idx in self.active_layers_indices:
Expand Down